Modern Data Stack 2025: Complete Guide for $10/Month | Data Engineering Solutions
As data engineering specialists in the Stockholm area, we’ve helped companies build cost-effective data stacks that deliver enterprise-quality solutions for a fraction of traditional costs. In this guide, we share exactly how to build a modern data stack for only $10/month.
What is a Modern Data Stack?
A modern data stack is a collection of cloud-native tools that work together to:
- Collect data from various sources
- Transform and model data for analysis
- Visualize insights for decision-making
- Automate workflows for scalability
Why “Modern”?
Unlike legacy systems, modern data stacks are:
- ✅ Cloud-native - no infrastructure management
- ✅ Serverless - pay only for what you use
- ✅ SQL-first - familiar to most analysts
- ✅ Version-controlled - code your data logic
The Complete $10/Month Architecture
Core Components (Total: ~$10/month)
1. Data Warehouse: Snowflake (~$5/month)
-- Create your first table
CREATE TABLE customer_analytics (
customer_id INT,
order_date DATE,
revenue DECIMAL(10,2),
region VARCHAR(50)
);
Why Snowflake:
- Automatic scaling
- Separated compute/storage
- Excellent performance for small datasets
2. Data Transformation: dbt Core (Free)
# models/customer_metrics.sql
SELECT
region,
DATE_TRUNC('month', order_date) as month,
COUNT(DISTINCT customer_id) as unique_customers,
SUM(revenue) as total_revenue
FROM {{ ref('customer_analytics') }}
GROUP BY 1, 2
3. Orchestration: GitHub Actions (Free)
name: Data Pipeline
on:
schedule:
- cron: '0 6 * * *' # Daily at 06:00
jobs:
run_dbt:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Run dbt
run: |
dbt deps
dbt run
dbt test
4. Visualization: Metabase (~$5/month on DigitalOcean)
- Open source BI tool
- Easy to deploy
- Powerful dashboards
Implementation Guide: Step-by-Step
Step 1: Set up Snowflake (15 minutes)
# 1. Register free Snowflake account
# 2. Create warehouse with AUTO_SUSPEND=60
CREATE WAREHOUSE dev_wh
WITH WAREHOUSE_SIZE = 'X-SMALL'
AUTO_SUSPEND = 60;
# 3. Create database and schema
CREATE DATABASE analytics;
CREATE SCHEMA analytics.staging;
Step 2: Install dbt (10 minutes)
# Install dbt-snowflake
pip install dbt-snowflake
# Initialize project
dbt init my_data_project
cd my_data_project
# Configure connection
# ~/.dbt/profiles.yml
my_data_project:
target: dev
outputs:
dev:
type: snowflake
account: YOUR_ACCOUNT.eu-west-1
user: YOUR_USERNAME
password: YOUR_PASSWORD
role: ACCOUNTADMIN
database: ANALYTICS
warehouse: DEV_WH
schema: staging
Step 3: First Data Pipeline (20 minutes)
-- models/staging/stg_orders.sql
SELECT
order_id,
customer_id,
order_date::DATE as order_date,
amount::DECIMAL(10,2) as amount,
status
FROM {{ source('raw', 'orders') }}
WHERE order_date >= '2024-01-01'
-- models/marts/customer_summary.sql
SELECT
customer_id,
COUNT(*) as total_orders,
SUM(amount) as lifetime_value,
MAX(order_date) as last_order_date
FROM {{ ref('stg_orders') }}
GROUP BY customer_id
Steg 4: Automatisering med GitHub Actions
# .github/workflows/data_pipeline.yml
name: Daily Data Pipeline
on:
schedule:
- cron: '0 6 * * *'
jobs:
run_pipeline:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v3
- name: Setup Python
uses: actions/setup-python@v4
with:
python-version: '3.9'
- name: Install dependencies
run: |
pip install dbt-snowflake
dbt deps
- name: Run dbt pipeline
env:
DBT_PROFILES_DIR: .
run: |
dbt run --target prod
dbt test --target prod
Kostnadsuppdelning & Optimering
Månadsvis Kostnadskalkyl
Komponent | Kostnad | Kommentar |
---|---|---|
Snowflake | $2-8 | Beroende på användning |
Metabase (DigitalOcean) | $5 | Droplet $5/månad |
dbt Core | Gratis | Open source |
GitHub Actions | Gratis | 2000 minuter/månad |
Total | $7-13 | Genomsnitt $10 |
Optimeringstips för Kostnadskontroll
-- 1. Använd AUTO_SUSPEND för warehouses
ALTER WAREHOUSE dev_wh SET AUTO_SUSPEND = 60;
-- 2. Schemalägg tunga queries under lågtrafik
-- 3. Använd CLUSTERING för stora tabeller
-- 4. Monitorer QUERY_HISTORY för kostnadsdrivare
SELECT
query_text,
total_elapsed_time/1000 as seconds,
credits_used_cloud_services
FROM table(information_schema.query_history())
WHERE start_time >= DATEADD(day, -7, CURRENT_TIMESTAMP())
ORDER BY credits_used_cloud_services DESC
LIMIT 10;
Svenska Företag & GDPR Compliance
Datahantering enligt GDPR
-- Implementera data retention policies
CREATE TABLE customer_data_retention (
customer_id INT,
consent_date DATE,
retention_period INT -- dagar
);
-- Automated deletion efter consent period
DELETE FROM customer_analytics
WHERE customer_id IN (
SELECT customer_id
FROM customer_data_retention
WHERE DATEDIFF(day, consent_date, CURRENT_DATE) > retention_period
);
Svenska Molnregioner
För svenska företag rekommenderar jag:
- Snowflake EU-West-1 (Irland) för GDPR compliance
- Azure Sweden Central för extra säkerhet
- Dokumentera dataflöden för GDPR-audit
Hockey Analytics Case Study
Som expert på hockey analytics har jag implementerat denna stack för svenska ishockeylag:
-- Hockey performance metrics
WITH player_stats AS (
SELECT
player_id,
game_date,
goals,
assists,
shots_on_goal,
ice_time_seconds
FROM {{ ref('stg_game_logs') }}
),
performance_metrics AS (
SELECT
player_id,
AVG(goals + assists) as avg_points_per_game,
AVG(shots_on_goal) as avg_shots_per_game,
SUM(ice_time_seconds)/3600.0 as total_ice_time_hours
FROM player_stats
WHERE game_date >= DATEADD(month, -3, CURRENT_DATE)
GROUP BY player_id
)
SELECT * FROM performance_metrics
WHERE avg_points_per_game > 0.5 -- Filter for productive players
Nästa Steg: Skalning & Advanced Features
När du växer över $10/månad budgeten:
- Lägg till Reverse ETL - Hightouch eller Census
- Implementera Data Quality - Great Expectations
- Advanced Orchestration - Prefect eller Dagster
- Real-time Processing - Kafka + ksqlDB
Professionell Support
Behöver du hjälp att implementera din moderna data stack? Som data engineer i Nacka erbjuder jag:
- Arkitektur-rådgivning för svenska företag
- Hands-on implementation av kompletta pipelines
- GDPR-compliance för europeiska regioner
- Hockey analytics specialisering
Sammanfattning
En modern data stack för $10/månad är inte bara möjlig - det är praktiskt för de flesta små till medelstora företag. Nyckeln är att:
- Starta enkelt med proven verktyg
- Automatisera tidigt för skalbarhet
- Monitera kostnader kontinuerligt
- Skala smart när behoven växer
Som data engineer i Stockholm-området har jag sett denna approach spara företag tusentals kronor månaden samtidigt som de får enterprise-kvalitet på sin data infrastructure.
Vill du implementera din egen moderna data stack? Kontakta mig för personlig rådgivning kring data engineering, hockey analytics eller AI-automatisering i Stockholm-området.
Emil Karlsson är data engineer och hockey analytics specialist baserad i Nacka, Sverige. Specialiserat på kostnadseffektiva data solutions för svenska företag.